Measuring the Content of Presidential Policy Making: Applying Text Analysis to Executive Branch Directives

Abstract

The executive branch produces huge quantities of text data: policy‐driving documents such as executive orders, national security directives, and agency regulations; procedural documents such as notices of proposed rule making, meeting transcripts, and presidential daily schedules; and public relations documents including press releases, veto statements, and social media posts. Political science is increasingly turning to methods of automated text analysis to rigorously interrogate such corpora. These tools have proved invaluable to scholars of the judiciary, of the legislature, and of political behavior; they hold great promise for the study of the presidency and bureaucracy. These methods' value lies in measuring complex, nuanced, yet theoretically critical quantities of interest like power, agenda setting, policy significance, ideology, and diplomatic resolve: by categorizing individual documents into a researcher‐defined schema, automated text methods produce large‐N data sets with fine temporal granularity. Recent work using text analysis to study unilateral actions illustrates these methods' promise for upturning conventional wisdom, settling long‐standing debates, and illuminating new puzzles in executive politics in the United States.

Junctures in the time of COVID-19: Topic search and government's framing of COVID-19 response in the Philippines

“…However, as is typical in studies of this type, our interest is precisely to read the government's policy position from a corpus consisting of its own statements. This is consistent with studies that analyze political texts from unilateral sources such as party manifestos ( Eder et al, 2017 ), party elite interviews ( Ecker et al, 2022 ), and public pronouncements or speeches by key political actors such as chief executives ( Kaufman, 2020 ; Panao and Pernia, 2022 ).…”

The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis

“…For extensive collections of texts, they also advocate for machine learning in the legal domain as tools to accelerate analysis and give insights about the content of documents, though still not replacing humans, only facilitating some processes. Kaufman [ 6 ] in turn presents potential applications of machine learning in the future of political science analysis in the Executive Branch of the United States.…”

“…Computational tools also pave the way to a new era of technological innovation in the realm of politics. In fact, current computational tools act as a novel utility belt to evaluate and validate long-term theories [ 6 ] in the field of Political Science. They also facilitate the citizens’ access to federal government decisions and enhance the public control of the executive direct actions.…”

“…Regarding the Executive Branch, on the other hand, still few works present computational results applied to direct acts of the president [ 10 ]. However, computational analysis is a promising approach to explore documents in an executive-centric system [ 6 ], highly dependent on presidential decisions, as Brazil. With this work, we aim to illuminate some of the aspects of the executive policy-making in the Federal Administration in Brazil through the computational analysis of administrative decrees.…”

Under Pressure: Centralizing Regulation in Response to Presidential Priorities

“…Finally, we introduce two novel datasets that allow us to examine when presidents draw public attention to the administrative state and how executive agencies organize regulatory policy. Just as congressional and judicial scholars have utilized institutional text to shed light on key questions in political science, we analyze the Public Papers of the Presidents and administrative records to identify new descriptive patterns in executive behavior (see Kaufman 2020). By examining the text of the executive branch, we shed light on the nuanced dynamics of political control.…”